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Google Cloud Certified Professional-Data-Engineer Googleの試験はどうですか。
Google Cloud Certified Professional-Data-Engineer的中合格問題集 - Google Certified Professional Data Engineer Exam 近年、IT業種の発展はますます速くなることにつれて、ITを勉強する人は急激に多くなりました。 それと比べるものがありません。専門的な団体と正確性の高いGoogleのProfessional-Data-Engineer 日本語版参考資料問題集があるこそ、Goldmile-Infobizのサイトは世界的でProfessional-Data-Engineer 日本語版参考資料試験トレーニングによっての試験合格率が一番高いです。
Goldmile-InfobizのGoogleのProfessional-Data-Engineer的中合格問題集試験トレーニング資料は全てのオンラインのトレーニング資料で一番よいものです。我々の知名度はとても高いです。これは受験生の皆さんが資料を利用した後の結果です。
Google Professional-Data-Engineer的中合格問題集 - 人生には様々な選択があります。
弊社のGoogleのProfessional-Data-Engineer的中合格問題集試験問題集を買うかどうかまだ決めていないなら、弊社のデモをやってみよう。使用してから、あなたは弊社の商品でGoogleのProfessional-Data-Engineer的中合格問題集試験に合格できるということを信じています。我々Goldmile-Infobizの専門家たちのGoogleのProfessional-Data-Engineer的中合格問題集試験問題集への更新と改善はあなたに試験の準備期間から成功させます。
このような保証があれば、Goldmile-InfobizのProfessional-Data-Engineer的中合格問題集問題集を購入しようか購入するまいかと躊躇する必要は全くないです。この問題集をミスすればあなたの大きな損失ですよ。
Professional-Data-Engineer PDF DEMO:
QUESTION NO: 1
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning.
What should you do?
A. Build and train a text classification model using TensorFlow. Deploy the model using Cloud
Machine Learning Engine. Call the model from your application and process the results as labels.
B. Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.
C. Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes
Engine cluster. Call the model from your application and process the results as labels.
D. Call the Cloud Natural Language API from your application. Process the generated Sentiment
Analysis as labels.
Answer: D
QUESTION NO: 2
Your company is using WHILECARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:
# Syntax error : Expected end of statement but got "-" at [4:11]
SELECT age
FROM
bigquery-public-data.noaa_gsod.gsod
WHERE
age != 99
AND_TABLE_SUFFIX = '1929'
ORDER BY
age DESC
Which table name will make the SQL statement work correctly?
A. 'bigquery-public-data.noaa_gsod.gsod*`
B. 'bigquery-public-data.noaa_gsod.gsod'*
C. 'bigquery-public-data.noaa_gsod.gsod'
D. bigquery-public-data.noaa_gsod.gsod*
Answer: A
QUESTION NO: 3
MJTelco is building a custom interface to share data. They have these requirements:
* They need to do aggregations over their petabyte-scale datasets.
* They need to scan specific time range rows with a very fast response time (milliseconds).
Which combination of Google Cloud Platform products should you recommend?
A. Cloud Datastore and Cloud Bigtable
B. Cloud Bigtable and Cloud SQL
C. BigQuery and Cloud Bigtable
D. BigQuery and Cloud Storage
Answer: C
QUESTION NO: 4
You have Cloud Functions written in Node.js that pull messages from Cloud Pub/Sub and send the data to BigQuery. You observe that the message processing rate on the Pub/Sub topic is orders of magnitude higher than anticipated, but there is no error logged in Stackdriver Log Viewer. What are the two most likely causes of this problem? Choose 2 answers.
A. Publisher throughput quota is too small.
B. The subscriber code cannot keep up with the messages.
C. The subscriber code does not acknowledge the messages that it pulls.
D. Error handling in the subscriber code is not handling run-time errors properly.
E. Total outstanding messages exceed the 10-MB maximum.
Answer: B,D
QUESTION NO: 5
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?
A. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
B. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query
BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
C. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
D. Load the data every 30 minutes into a new partitioned table in BigQuery.
Answer: D
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Updated: May 27, 2022